Cost-effectiveness of implementing automated grading within the national screening programme for diabetic retinopathy in Scotland

Aims: National screening programmes for diabetic retinopathy using digital photography and multi-level manual grading systems are currently being implemented in the UK. Here, we assess the cost-effectiveness of replacing first level manual grading in the National Screening Programme in Scotland with an automated system developed to assess image quality and detect the presence of any retinopathy. Methods: A decision tree model was developed and populated using sensitivity/specificity and cost data based on a study of 6722 patients in the Grampian region. Costs to the NHS, and the number of appropriate screening outcomes and true referable cases detected in 1 year were assessed. Results: For the diabetic population of Scotland (approximately 160 000), with prevalence of referable retinopathy at 4% (6400 true cases), the automated strategy would be expected to identify 5560 cases (86.9%) and the manual strategy 5610 cases (87.7%). However, the automated system led to savings in grading and quality assurance costs to the NHS of £201 600 per year. The additional cost per additional referable case detected (manual vs automated) totalled £4088 and the additional cost per additional appropriate screening outcome (manual vs automated) was £1990. Conclusions: Given that automated grading is less costly and of similar effectiveness, it is likely to be considered a cost-effective alternative to manual grading.

[1]  J. Olson,et al.  The efficacy of automated “disease/no disease” grading for diabetic retinopathy in a systematic screening programme , 2007, British Journal of Ophthalmology.

[2]  P Barton,et al.  Cervical screening programmes: can automation help? Evidence from systematic reviews, an economic analysis and a simulation modelling exercise applied to the UK. , 2005, Health technology assessment.

[3]  P F Sharp,et al.  The value of digital imaging in diabetic retinopathy. , 2003, Health technology assessment.

[4]  L. Curtis,et al.  Unit Costs of Health and Social Care 2016 , 2015 .

[5]  P. Taylor,et al.  Impact of computer-aided detection prompts on the sensitivity and specificity of screening mammography. , 2005, Health technology assessment.

[6]  Lloyd Paul Aiello,et al.  Preventive Eye Care in People With Diabetes Is Cost-Saving to the Federal Government: Implications for health-care reform , 1994, Diabetes Care.

[7]  Bram van Ginneken,et al.  Automatic detection of red lesions in digital color fundus photographs , 2005, IEEE Transactions on Medical Imaging.

[8]  S. Brailsford,et al.  The evaluation of screening policies for diabetic retinopathy using simulation , 2002, Diabetic medicine : a journal of the British Diabetic Association.

[9]  J. Olson,et al.  Automated assessment of diabetic retinal image quality based on clarity and field definition. , 2006, Investigative ophthalmology & visual science.

[10]  M. Larsen,et al.  Automated detection of fundus photographic red lesions in diabetic retinopathy. , 2003, Investigative ophthalmology & visual science.

[11]  Marilyn James,et al.  Cost effectiveness analysis of screening for sight threatening diabetic eye disease , 2000, BMJ : British Medical Journal.

[12]  David Maberley,et al.  Screening for diabetic retinopathy in James Bay, Ontario: a cost-effectiveness analysis. , 2003, CMAJ : Canadian Medical Association journal = journal de l'Association medicale canadienne.

[13]  J. Olson,et al.  Automated detection of microaneurysms in digital red‐free photographs: a diabetic retinopathy screening tool , 2000, Diabetic medicine : a journal of the British Diabetic Association.